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MIT OpenCourseWareMIT 6.00 Introduction to Computer Science and Programming, Fall 2008 (2009)

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This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Pythonâ„¢ programming language.


This movie is part of the collection: MIT OpenCourseWare

Producer: MIT OpenCourseWare
Audio/Visual: sound, color
Language: English
Keywords: Python; programming; computer science; computation; problem solving; recursion; binary search; classes; inheritance; libraries; algorithms; optimization problems; modules; simulation; big O notation; control flow; exceptions; building computational models; software engineering

Creative Commons license: Attribution-Noncommercial-Share Alike 3.0


Individual Files

Movie Files MPEG4 Ogg Video
Lecture 01: Goals of the course; what is computation; introduction to data types, operators, and variable 116.4 MB 
212.6 MB 
Lecture 02: Operators and operands; statements; branching, conditionals, and iteration 110.7 MB 
195.1 MB 
Lecture 03: Common code patterns: iterative programs 110.6 MB 
200.5 MB 
Lecture 04: Decomposition and abstraction through functions; introduction to recursion 112.5 MB 
185.1 MB 
Lecture 05: Floating point numbers, successive refinement, finding roots 96.3 MB 
171.4 MB 
Lecture 06: Bisection methods, Newton/Raphson, introduction to lists 109.5 MB 
164.2 MB 
Lecture 07: Lists and mutability, dictionaries, pseudocode, introduction to efficiency 101.0 MB 
173.5 MB 
Lecture 08: Complexity; log, linear, quadratic, exponential algorithms 109.0 MB 
183.3 MB 
Lecture 09: Binary search, bubble and selection sorts 103.3 MB 
185.6 MB 
Lecture 10: Divide and conquer methods, merge sort, exceptions 100.7 MB 
175.8 MB 
Lecture 11: Testing and debugging 106.7 MB 
186.8 MB 
Lecture 12: More about debugging, knapsack problem, introduction to dynamic programming 108.1 MB 
201.8 MB 
Lecture 13: Dynamic programming: overlapping subproblems, optimal substructure 106.8 MB 
173.9 MB 
Lecture 14: Analysis of knapsack problem, introduction to object-oriented programming 109.9 MB 
208.9 MB 
Lecture 15: Abstract data types, classes and methods 110.3 MB 
182.1 MB 
Lecture 16: Encapsulation, inheritance, shadowing 110.1 MB 
175.8 MB 
Lecture 17: Computational models: random walk simulation 107.6 MB 
185.5 MB 
Lecture 18: Presenting simulation results, Pylab, plotting 115.6 MB 
182.5 MB 
Lecture 19: Biased random walks, distributions 108.8 MB 
177.9 MB 
Lecture 20: Monte Carlo simulations, estimating pi 104.8 MB 
176.7 MB 
Lecture 21: Validating simulation results, curve fitting, linear regression 116.9 MB 
207.5 MB 
Lecture 22: Normal, uniform, and exponential distributions; misuse of statistics 110.4 MB 
195.3 MB 
Lecture 23: Stock market simulation 111.8 MB 
192.7 MB 
Lecture 24: Course overview; what do computer scientists do? 94.6 MB 
129.2 MB 
Image Files Thumbnail Animated GIF
Lecture 01: Goals of the course; what is computation; introduction to data types, operators, and variable 4.8 KB 
416.7 KB 
Lecture 02: Operators and operands; statements; branching, conditionals, and iteration 5.0 KB 
421.0 KB 
Lecture 03: Common code patterns: iterative programs 6.4 KB 
439.1 KB 
Lecture 04: Decomposition and abstraction through functions; introduction to recursion 5.2 KB 
429.1 KB 
Lecture 05: Floating point numbers, successive refinement, finding roots 5.8 KB 
411.9 KB 
Lecture 06: Bisection methods, Newton/Raphson, introduction to lists 5.7 KB 
437.8 KB 
Lecture 07: Lists and mutability, dictionaries, pseudocode, introduction to efficiency 5.7 KB 
428.3 KB 
Lecture 08: Complexity; log, linear, quadratic, exponential algorithms 7.0 KB 
435.0 KB 
Lecture 09: Binary search, bubble and selection sorts 3.9 KB 
429.8 KB 
Lecture 10: Divide and conquer methods, merge sort, exceptions 6.5 KB 
432.9 KB 
Lecture 11: Testing and debugging 5.5 KB 
422.4 KB 
Lecture 12: More about debugging, knapsack problem, introduction to dynamic programming 6.8 KB 
438.2 KB 
Lecture 13: Dynamic programming: overlapping subproblems, optimal substructure 4.3 KB 
430.5 KB 
Lecture 14: Analysis of knapsack problem, introduction to object-oriented programming 6.0 KB 
442.4 KB 
Lecture 15: Abstract data types, classes and methods 4.1 KB 
435.6 KB 
Lecture 16: Encapsulation, inheritance, shadowing 7.7 KB 
430.8 KB 
Lecture 17: Computational models: random walk simulation 5.6 KB 
433.9 KB 
Lecture 18: Presenting simulation results, Pylab, plotting 7.3 KB 
421.8 KB 
Lecture 19: Biased random walks, distributions 5.1 KB 
424.6 KB 
Lecture 20: Monte Carlo simulations, estimating pi 5.6 KB 
427.2 KB 
Lecture 21: Validating simulation results, curve fitting, linear regression 6.1 KB 
436.5 KB 
Lecture 22: Normal, uniform, and exponential distributions; misuse of statistics 5.6 KB 
436.0 KB 
Lecture 23: Stock market simulation 6.1 KB 
425.6 KB 
Lecture 24: Course overview; what do computer scientists do? 5.0 KB 
320.2 KB 
Information FormatSize
MIT6.00F08_files.xml Metadata [file] 
MIT6.00F08_meta.xml Metadata 1.5 KB 

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